Firstly, a principle of vector quantization will be briefly explained. It is assumed here that signals included in a sequence of input signals from a source of informations is grounded K by K (K being a plural number) to provide input vectors x={x.sub.1, x.sub.2, . . . , x.sub.K }. It is also assumed here that an output vector y.sub.i ={y.sub.i,1, y.sub.i,2, . . . , y.sub.i,K } corresponds to a representative point (for example, a center of gravity) of a partial space R.sub.i which is one of N divisions R.sub.1, R.sub.2, . . . , R.sub.N of K-dimensional Euclidean Signal Space R.sup.K (x.epsilon.R.sup.K), and that a set of output vectors is represented as Y={y.sub.1, y.sub.2, . . . , y.sub.N }. Then vector quantization Q is defined by the following equations: EQU Q: R.sup.K .fwdarw.Y
wherein, ##EQU1## The above-mentioned vector quantization Q is represented as a cascade connection of coding C and decoding D. Coding C is a mapping of the set Y={y.sub.1, y.sub.2, . . . , y.sub.N } of the output vectors in R.sup.K into an index set (coded output) I={1, 2, . . . , N}, while decoding D is a mapping from I to Y. In other words, EQU C: R.sup.K .fwdarw.I, D: I.fwdarw.Y EQU Q=D.multidot.C.
Thus, in vector quantization, said index set I is transmitted or recorded, so the coding efficiency is greatly enhanced. Vector quantization is defined as a mapping of the input vector x into the output vector y.sub.i which is positioned at the shortest distance from the input vector (thus providing a minimum distortion). To be concrete, if a distance between the input vector x and the output vector y.sub.i (i.e., a distortion) is assumed to be represented as d(x, y.sub.i), the following relation is obtained: EQU if d(x, y.sub.i)&lt;d(x, y.sub.j) for all j x.epsilon.R.sub.i, namely x.fwdarw.y.sub.i.
The set Y of the output vectors y.sub.i as shown in FIG. 1 can be obtained by clustering algorithm by use of a sequence of input signals from the source of informations which serves as a training model (the clustering algorithm means a series of operations or repeating selection of representative points and division of the signal space until the sum of the distortions reaches to a minimum value).
FIG. 2 is a block diagram showing a construction of a vector quantization coder of the prior art. In FIG. 2, the numeral 1 designates an input vector register (I.V.R.), numeral 2 a code table address counter (C.T.A.C.), numeral 3 an output vector code table memory (O.V.C.T.M.), numeral 4 a code table output vector register (C.T.O.V.R.), numeral 5 a parallel subtractor (P.S.), numeral 6 a parallel absolute value arithmetic unit (P.A.V.A.U.), numeral 7 an absolute value distortion detector (A.V.D.D.), numeral 8 a minimum distortion output vector detector (M.D.O.V.D.) and numeral 9 an index latch (I.L.) The output vector code table memory 3 prestores a set of the output vectors converged so as to make the sum of the distortions minimum by clustering algorithm.
In operation of the vector quantization coder of the prior art shown in FIG. 2, signals included in a sequence of input signals are grouped K by K into signal blocks and each block is taken in by the input vector register 1 as the input vector x={x.sub.1, x.sub.2, . . . , x.sub.K }. As the code table address counter 2 counts up in sequence from i=1 to i=N, the output vectors y.sub.i ={y.sub.i,1, y.sub.i,2, . . . , y.sub.i,K } are read out of the output vector code table memory 3 in sequence and latched into the code table output vector register 4. For each output vector y.sub.i, the absolute value distortion d.sub.i between the input and the output vectors is calculated by means of the following equation by the parallel subtractor 5, the parallel absolute value arithmetic unit 6 and the absolute value distortion detector 7: ##EQU2##
Then the minimum distortion output vector detector 8 detects the output vector which makes said absolute value distortion d.sub.i minimum. The minimum distortion d is given as follows: ##EQU3## The minimum distortion output vector detector 8 calculates the distortion d(x, y.sub.i) between the input vector x and each of output vectors y.sub.i read in sequence out of the output vector code table memory 3, and compares said distortion with the minimum distortion previously obtained. When a smaller value is detected, the detector 8 holds it as a new minimum distortion, and, whenever such a smaller value is obtained, transmits a strobe signal to the index latch 9 which, in turn, receives the index signal; which is the code table address of the output vector y.sub.i. The above-mentioned procedure is repeated until all the output vectors y.sub.i (i=1.about.N) are read out of the output vector code table memory 3, whereby a full search operation may be completed. Upon completion of such an operation, the index signal i of the output vector y.sub.i which produces the minimum distortion d remains in the index latch 9 and this value is a coded output. In a decoder the output vector which corresponds to the index signal i sent from the coder is read out of the output vector code table memory, whereby video signals are reproduced.
Since the vector quantization coder of the prior art is constructed as described above, the coder has serious drawbacks. The dimensional number K (i.e., a block size), once established, is unchanging. As a result, it is impossible to use the vector quantization coder of the same construction for other block sizes suitable for characteristics of a sequence of input signals. Moreover, the construction of the device is complicated.